Machine Learning Meets Quantum Science

The recent advancement of machine learning provides new opportunities for tackling challenges in quantum science, ranging from condensed matter physics, high energy physics to quantum information science. In this talk, I will first discuss a class of anti-symmetric wave functions based on neural network backflow,  which is efficient for simulating strongly-correlated lattice models and artificial quantum materials. Next, I will talk about recent progress of simulating continuum quantum field theories with neural quantum field state, and lattice gauge theories such as 2+1D quantum electrodynamics with finite density dynamical fermions using gauge symmetric neural networks. I will further discuss neural network representation based on positive-value-operator and phase space measurements for quantum dynamics simulations. Finally, I will present applications of machine learning in quantum control, quantum optimization and quantum machine learning.

Zoom link:  https://pitp.zoom.us/j/93834456412?pwd=R0hxdEpxanFFRnZmTHlqZTBXRi82QT09